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Not All Products Behave the Same, So Why Forecast Them That Way? Imagine trying to forecast daily demand for toothpaste the same way you would for holiday gift baskets. One sells steadily year-round, the other spikes unpredictably. Yet in many businesses, all products are still forecasted using the same models, with the same assumptions, and often with the same level of effort. The result? Overstocked warehouses, stockouts during peak demand, and wasted hours spent fine-tunin

For decades, demand forecasting followed a relatively clear path. Forecasters relied on what customers expressed through purchases, search queries, and seasonal trends. These signals were observable, repeatable, and mostly predictable. Forecasting models based on historical data could produce reliable estimates. Supply chains could plan with a reasonable degree of confidence. That world is fading. Today, social media is reshaping how people discover products, interact with co

More and more businesses are using integrated AI and machine learning (ML) models within their ERP systems to forecast product demand but many struggle to explain these forecasts to management and colleagues. Why? Because AI and ML models are complex and operate in high-dimensional spaces, which are harder to understand than traditional models based on simpler, linear relationships. Even though these models have demonstrated that they can improve forecast accuracy, they are d